FRS demonstrates highly accurate recognition rates, which has led to their widespread usage in eCommerce, online banking, surveillance and security applications. The recent advances in the deep learning techniques have further increased the accuracy of the FRS  [1]},  [2]} that enabled them to be deployed in the border control applications. However, the FRS is vulnerable to various attacks, among which the face morphing attacks have gained attention due to their impact on the border control application. Recent benchmarking results reported in NIST FRVT MOPRH [3]} indicate that the higher the accuracy of the FRS, the higher the vulnerability for the morphing attacks.
